A machine learning framework for predicting downstream water end-use events with upstream sensors

Abstract

Understanding the end-use of water is essential to a plethora of critical research in premise plumbing. However, direct access to end-use data through physical sensors is prohibitively expensive for most researchers, building owners, operators, and practitioners. Therefore, machine learning models can alleviate these costs by predicting downstream end-use events (e.g., sink, shower, dishwasher, and washing machine) via an affordable subset of upstream sensors. Choosing which upstream sensors, as well as data preprocessing methods, are best for machine learning has historically been a manual process. This paper proposes a novel approach to systematically configure the machine platform automatically. The optima were determined through a Pareto analysis of the exhaustive combinations of upstream predictors and preprocessing methods. The model was trained and validated with real-world data obtained from a house that has been extensively monitored for over a year. Results from the analysis suggested that downstream events can be effectively predicted with minimum overfitting error for most categories, using as few as two to four upstream sensors. This study automatically implemented highly accurate machine learning models to predict downstream features within premise plumbing systems, significantly lowering the costs of researching residential plumbing best practices such as water conservation.

Predictive capability of THM models for drinking water treatment and distribution

“…There have been a growing amount of research exploring the use of ML based techniques for predicting THMs. [51][52][53][54][55][56][57] Most of the research uses some type of artificial neural network (ANN) based model to develop non-linear relationships between water quality variables and THM concentration. The ML based approaches show promise by demonstrating lower error compared to their multiple linear regression based model counterparts.…”